Paper: 298637
Title: Neural Network Congestion Control Design for TCP/IP Network Based Genetic Algorithm

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PAPER: 2
TITLE: Neural Network Congestion Control Design for TCP/IP Network Based Genetic Algorithm
 
OVERALL RATING: 2 (accept) 
REVIEWER'S CONFIDENCE: 2 (medium) 
Relevance to this conference: 4 (good)
Originality/Uniqueness: 4 (good)
English readability: 3 (fair)
Paper organization/presentation: 3 (fair)
Has good survey been done?: 3 (fair)


COMMENTS:
1. On page 3, First equation should be PI, should not it.
2. The authors describe the use of a dynamic recurrent neural model.
However, Fig. 5 shows only a feedforward network. I wonder where the recurrent
neural network involves in the controller.
3. Although, the authors describe the use of GA for changing weights of the network,
it is still unclear how the weights of the network are really updated. 
For clarity, I would recommend to also present the equations of weight changes. 




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Paper: 346758
Title: Models of Cognitive Evolution: Initial Steps

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PAPER: 5
TITLE: Models of Cognitive Evolution: Initial Steps
 
OVERALL RATING: 2 (accept) 
REVIEWER'S CONFIDENCE: 2 (medium) 
Relevance to this conference: 4 (good)
Originality/Uniqueness: 3 (fair)
English readability: 4 (good)
Paper organization/presentation: 4 (good)
Has good survey been done?: 3 (fair)


The paper is relevant to the theme of the conference; it presents some approaches to modeling cognitive evolution, albeit on quite primitive tasks, far from practice. The article is written in good English, although sometimes there are typos (in abstract and probably couple of brackets are missed in the expression 2). The material is consistent and well organized, but in my opinion attention paid to consideration of similar approaches is not enough. 




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Paper: 349269
Title: Toward a Moving Object Identification by Spiking Neurons

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PAPER: 11
TITLE: Toward a Moving Object Identification by Spiking Neurons
 
OVERALL RATING: -1 (weak reject) 
REVIEWER'S CONFIDENCE: 2 (medium) 
Relevance to this conference: 4 (good)
Originality/Uniqueness: 2 (poor)
English readability: 4 (good)
Paper organization/presentation: 3 (fair)
Has good survey been done?: 1 (very poor)


In general, the paper is written poorly, that was stated by its authors. The question arises, why we should use neural network (in particular spiking neural networks) to solve such problem (there are a lot of methods to identify moving object). There are two obvious answers, one is because they give a significant advantage compared with other methods (not shown in the paper), and the second is that it helps to facilitate interaction with other neural networks (also not reflected in the paper). There is no theoretical or experimental confirmation of the effectiveness of the proposed method for moving object identification. The neural network (section 2.3) used by authors is described quite blurry, resulting in doubts whether it can identify a moving object at all. I'd recommend to accept the report only if authors give at least theoretical or experimental (better both) justification of the effectiveness of proposed method. Article written at fairly good English, and in this respect, there's no any reproaches. 




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Paper: 350639
Title: Parallel and Distributed Evolutionary Computation: A Survey

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PAPER: 14
TITLE: Parallel and Distributed Evolutionary Computation: A Survey
 
OVERALL RATING: 0 (borderline paper) 
REVIEWER'S CONFIDENCE: 2 (medium) 
Relevance to this conference: 4 (good)
Originality/Uniqueness: 2 (poor)
English readability: 3 (fair)
Paper organization/presentation: 2 (poor)
Has good survey been done?: 3 (fair)


COMMENTS:
1. The paper provides a fair review of the parallel and distributed evolutionary computation.
2. The comparison part of the presented approaches are missing.
3. The order of the figures is irregular, i.e. the order begins from Fig. 4.1 of section 2.2.
4. The paper only refers to the citations no. 4, 5, 6, and 22 from the references; the rest are not been cited in the paper.